Best AI Coding Workflow for Radiology Groups
High volume, short reports, and a code set where a single word changes the CPT.
Radiology is the highest-volume, lowest-per-unit-time coding work in medicine. A group reading 400 studies a day cannot afford thirty seconds of coding overhead per study, and that constraint shapes everything about the right workflow.
The good news: radiology reports are short, structured by convention, and unusually machine-readable. The category is a strong fit. The bad news is that the errors that do occur are systematic, and systematic errors at 400 studies a day compound quickly.
The three recurring error classes
Contrast documentation is the first. With, without, or with and without changes the CPT, and the report frequently states it in the technique line rather than the impression — which is where summary-driven tools look.
Laterality is the second, and in radiology it is often the difference between a clean claim and a denial that costs more to appeal than the study earned.
The third is bundling on multi-region studies. A CT of the abdomen and pelvis is not two codes, and tools that key off region mentions get this wrong in a consistent direction.
Error concentration
Share of radiology coding errors observed by class in review work.
Three classes account for over 80%. A workflow that eliminates those three is most of the available benefit.
The workflow that fits the volume
- Read the full report, technique line included — Not the impression. Contrast and technique detail live above it and determine the code.
- Auto-accept high-confidence, evidence-linked codes — At radiology volume, routing every study to human review defeats the purpose. Route by confidence, not by default.
- Hard-stop the three error classes — Any study where contrast, laterality, or bundling is ambiguous goes to a human regardless of confidence.
- Batch-review the exceptions — A coder working a filtered exception queue at radiology volume is dramatically more productive than one working a full queue.
- Instrument the auto-accepted tail — Sample it weekly. Auto-acceptance without sampling is how systematic error goes unnoticed for a quarter.
At $0.18 average processing cost per encounter, the per-study economics work at any radiology volume. The constraint is never cost — it is whether the exception rate is low enough that human review stays a queue rather than a bottleneck.
At 400 studies a day, a workflow that requires human review of every study is not a workflow. It is a staffing plan.
What to measure in a radiology pilot
Exception rate first, accuracy second. A system with 98% accuracy that routes 40% of studies to review has not helped you.
Then measure the three error classes specifically against your prior-period denials. If contrast and laterality denials do not fall, nothing else matters.